Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
A Performance Analysis of Self- Evolutionary Algorithms on Networks with Correlated Failures Read chapter Read first chapter

2018 | OriginalPaper | Chapter

A Recommender System Based on Hierarchical Clustering for Cloud e-Learning

Authors : Krenare Pireva, Petros Kefalas

Published in: Intelligent Distributed Computing XI

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Cloud e-Learning (CeL) is a new paradigm for e-Learning, aiming towards using any possible learning object from the cloud in a smart way and generate a personalised learning path for individual learners. An issue that appears before the generation of the learning path through automated planning, is to filter a pool of resources that are relevant to the learners profile and desires in order to enhance their knowledge and skills at a higher cognitive level. In this paper, we present a Recommender System for Cloud e-Leaning (CeLRS) that uses hierarchical clustering to select the most appropriate resources and utilise a vector space model to rank these resources in order of relevance for any individual learner. We discuss the issues raised and we demonstrate how CeLRS works.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter An Argumentative Approach to Smart Home Office Ambient Lighting
next chapter A Taxonomy of Anomalies in Distributed Cloud Systems: The CRI-Model
Footnotes
Literature
1.
Buzydlowski, J.W., Lin, X., Zhang, M., Cassel, L.N.: A comparison of a hierarchical tree to an associative map interface for the selection of classification terms. Proc. Am. Soc. IST 50(1), 1–4 (2013)
2.
Felder, R.M., Brent, R.: Understanding student differences. J. Eng. Educ. 94(1), 57–72 (2005)CrossRef
3.
Fu, X., Budzik, J., Hammond, K.J.: Mining navigation history for recommendation. In: Proceedings of IC on Intelligent UI, pp. 106–112. ACM (2000)
4.
Fung, B.C., Wang, K., Ester, M.: Hierarchical document clustering using frequent itemsets. In: Proceedings of IC on Data Mining, pp. 59–70. SIAM (2003)
5.
Gomez-Uribe, C.A., Hunt, N.: The netflix recommender system: algorithms, business value, and innovation. ACM TMIS 6(4), 13 (2016)
6.
Jabakji, A., Dag, H.: Improving item-based recommendation accuracy with user’s preferences on apache mahout. In: 2016 IEEE International Conference on Big Data (Big Data), pp. 1742–1749. IEEE (2016)
7.
Li, X., Chang, S.K.: A personalized e-learning system based on user profile constructed using information fusion. In: DMS, pp. 109–114 (2005)
8.
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)CrossRef
9.
Lu, J.: Personalized e-learning material recommender system. In: IC on information technology for application, pp. 374–379 (2004)
10.
Pireva, K., Kefalas, P., Cowling, A.: A Review of Automated Planning and its Application to Cloud e-Learning. Work in progress, Paper Submitted (2017)
11.
Pireva, K., Kefalas, P., Stamatopoulou, I.: Representation of Learning Objects in Cloud e-Learning. Work in progress, Paper Submitted (2017)
12.
Pireva, K., Kefalas, P.: The use of multi agent systems in cloud e-learning. In: Doctoral Student Conference on ICT, pp. 324–336 (2015)
13.
Polettini, N.: The vector space model in information retrieval-term weighting problem. Entropy, 1–9 (2004)
14.
Portugal, I., Alencar, P., Cowan, D.: The use of machine learning algorithms in recommender systems: a systematic review (2015). arXiv:​1511.​05263
15.
Ricci, F., Rokach, L., Shapira, B.: Introduction to Recommender Systems Handbook. Springer (2011)
16.
Rokach, L., Maimon, O.: Clustering Methods. In: Data Mining and Knowledge Discovery Handbook, pp. 321–352. Springer (2005)
17.
Schafer, J.B., Konstan, J., Riedl, J.: Recommender systems in e-commerce. In: Proceedings of Conference on Electronic Commerce, pp. 158–166. ACM (1999)
18.
Singh, V.K., Singh, V.K.: Vector space model: an information retrieval system. Int. J. Adv. Eng. Res. Stud. 141, 143 (2015)
19.
Stern, D.H., Herbrich, R., Graepel, T.: Matchbox: large scale online bayesian recommendations. In: Proceedings of IC on WWW, pp. 111–120. ACM (2009)
20.
Willett, P.: The porter stemming algorithm: then and now. Program 40(3), 219–223 (2006)CrossRef
Metadata
Title
A Recommender System Based on Hierarchical Clustering for Cloud e-Learning
Authors
Krenare Pireva
Petros Kefalas
Copyright Year
2018
Book
Intelligent Distributed Computing XI

Print ISBN: 978-3-319-66378-4

Electronic ISBN: 978-3-319-66379-1

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-66379-1_21

Premium Partner

    Image Credits